A capsule network includes layers of capsules where each capsule consists of a group of neurons representing the information being learned. Capsule networks [1]} are composed of primary and secondary layers.The first layer encapsulates the input using convolutional, reshaping, and squashing operations to represent the instantiating parameters, such as positional attributes, in the form of a vector. The secondary layer then learns deeper part-whole relations between the sub-parts of an object and the whole object.
In the CapsField solution proposed in this paper, the convolution operation has been removed from the primary capsule layer, so that only reshaping and squashing functions are applied to the spatial features coming from the previous sub-network. This modification is due to the fact that the previous CNN feature extraction sub-network already encodes the spatial information through several convolutional layers.
